SMILE models synonymy in multi-EHR codes via spherical mixtures of von Mises-Fisher distributions and develops a composite quasi-likelihood estimator with non-asymptotic error bounds and consistent cluster recovery.
arXiv preprint arXiv:2310.08459 , year=
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Synthetic noise domains serve as surrogate sources to tighten generalization bounds and improve performance in semi-supervised target domains via the proposed Noise Adaptation Framework.
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Spherical Mixture Integration for Latent Embedding Alignment across Multi-Source Feature Spaces
SMILE models synonymy in multi-EHR codes via spherical mixtures of von Mises-Fisher distributions and develops a composite quasi-likelihood estimator with non-asymptotic error bounds and consistent cluster recovery.
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Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain
Synthetic noise domains serve as surrogate sources to tighten generalization bounds and improve performance in semi-supervised target domains via the proposed Noise Adaptation Framework.